Warping Landmark Configurations

Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) . Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) and Grunstra et al. (2020) .

'Trial Sequential Analysis' for Error Control and Inference in Sequential Meta-Analyses

Frequentist sequential meta-analysis based on 'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen Trial Unit (CTU). The primary function is the calculation of group sequential designs for meta-analysis to be used for planning and analysis of both prospective and retrospective sequential meta-analyses to preserve type-I-error control under sequential testing. 'RTSA' includes tools for sample size and trial size calculation for meta-analysis and core meta-analyses methods such as fixed-effect and random-effects models and forest plots. TSA is described in Wetterslev et. al (2008) . The methods for deriving the group sequential designs are based on Jennison and Turnbull (1999, ISBN:9780849303166).

Truncated Functional Generalized Linear Models

An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) , including truncated functional linear and truncated functional logistic regression models.

Wraps 'libnabo', a Fast K Nearest Neighbour Library for Low Dimensions

An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points.

Translate Integers into English

Allow numbers to be presented in an English language version, one, two, three, ... Ordinals are also available, first, second, third, ... and indefinite article choice, "a" or "an".

Cross-Validated Area Under the ROC Curve Confidence Intervals

Tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators. The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively. One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods. The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.

Bayesian Modeling of Archaeological Chronologies

Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' ('JAGS' stands for Just Another Gibbs Sampler. It is a program for the analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation. See < http://mcmc-jags.sourceforge.net/> and "JAGS Version 4.3.0 user manual", Martin Plummer (2017) < https://sourceforge.net/projects/mcmc-jags/files/Manuals/>.). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.

Fits a Model that Partitions the Covariate Space into Blocks in a Data- Adaptive Way

Implements convex regression with interpretable sharp partitions (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 < http://jmlr.org/papers/volume17/15-344/15-344.pdf>.

Robust Selection Algorithm

An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) < http://proceedings.mlr.press/v108/cisneros20a.html>.

Four-Step Biodiversity Analysis Based on 'iNEXT'

Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) for statistical background.